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from functools import partial
from typing import Tuple, List
from .utils import (
CBLog,
LongCepoConfig,
get_prompt_response,
concurrent_map,
logger,
loop_until_match,
)
from .chunking import (
chunk_context,
get_prompt_length,
)
format_chunk_list = lambda chunk_list: [
f"Information of Chunk {index}:\n{doc}\n" for index, doc in enumerate(chunk_list)
]
def remove_chunks(chunks: List[str], irrelevance_tags: Tuple[str]) -> List[str]:
"""
Filter out chunks that contain at least one of irrelevance tags.
"""
new_chunks = []
for chunk in chunks:
# Skip None values resulting from failed API calls
if chunk is None:
continue
flag = False
for tag in irrelevance_tags:
# Ensure tag comparison is safe even if tag is None (though unlikely)
if tag and tag.upper() in chunk.upper():
flag = True
break
if not flag:
new_chunks.append(chunk)
return new_chunks
def mapreduce(
system_prompt: str,
query: str,
context: str,
qa_history: str,
client,
model: str,
tokenizer,
longcepo_config: LongCepoConfig,
cb_log: CBLog,
answer_tags: Tuple[str] = ("Answer:", "**Answer**:", "**Answer**"),
irrelevance_tags: Tuple[str] = ("[NO INFORMATION]",),
) -> Tuple[str, CBLog]:
"""
Executes a MapReduce-style inference pipeline to answer a query from long context.
The function splits the input context into chunks, summarizes and evaluates each with the model (Map),
collapses intermediate answers to reduce redundancy, and then generates a final answer (Reduce).
Irrelevant responses are filtered based on `irrelevance_tags`.
Args:
system_prompt (str): System prompt string.
query (str): User query.
context (str): Long-form input context to process.
qa_history (str): QA history string for prompt injection.
client: LLM API client.
model (str): Base model name.
tokenizer: Tokenizer to compute token lengths.
longcepo_config (LongCepoConfig): Config with hyper-parameters and token limits.
cb_log (CBLog): Log object for tracking model calls.
answer_tags (Tuple[str]): Tags used to extract the final answer from model output.
irrelevance_tags (Tuple[str]): Tags used to identify and remove irrelevant outputs.
Returns:
Tuple[str, CBLog]: Final extracted answer and updated log object.
"""
logger.info(f"MapReduce query: {query}")
qa_history_stub = (
f"\n\nAnswers to related questions:\n\n{qa_history}" if qa_history else ""
)
context_chunks = chunk_context(context, longcepo_config.chunk_size, tokenizer)
# Get short summaries of each chunk
def fetch_chunk_summary(client, model, chunk, query, system_prompt):
return get_prompt_response(
client,
model,
longcepo_config.summary_prompt.format(question=query, context=chunk),
system_prompt,
max_tokens=longcepo_config.max_output_tokens_summary,
temperature=longcepo_config.temperature_map,
)
summaries, cb_log = concurrent_map(
fetch_chunk_summary,
client,
model,
context_chunks,
query,
system_prompt,
cb_log,
)
num_summaries = longcepo_config.num_neighbor_summaries
# For each chunk position, get a neighborhood of `num_summaries` before and after the position
summaries_per_chunk = [
"\n\n".join(
summaries[
max(0, (summary_idx - num_summaries)) : min(
len(summaries) - 1, (summary_idx + num_summaries)
)
]
)
for summary_idx in range(len(summaries))
]
# Map stage
def fetch_map_response(client, model, chunk, query, system_prompt, summary):
return get_prompt_response(
client,
model,
longcepo_config.map_prompt.format(
question=query,
context=chunk,
summary=summary,
qa_history_stub=qa_history_stub,
),
system_prompt,
max_tokens=longcepo_config.max_output_tokens,
temperature=longcepo_config.temperature_map,
)
result, cb_log = concurrent_map(
fetch_map_response,
client,
model,
context_chunks,
query,
system_prompt,
cb_log,
summaries_per_chunk=summaries_per_chunk,
)
result = remove_chunks(result, irrelevance_tags)
if not result:
return "No information", cb_log
logger.info(
f"Removed {len(context_chunks) - len(result)} chunks from total {len(context_chunks)} chunks"
)
# Collapse stage
result, cb_log = collapse_chunks(
client,
model,
result,
query,
system_prompt,
qa_history_stub,
tokenizer,
cb_log,
longcepo_config,
)
result = remove_chunks(result, irrelevance_tags)
if not result:
return "No information", cb_log
# Reduce stage
prompt = longcepo_config.reduce_prompt.format(
question=query,
context=format_chunk_list(result),
qa_history_stub=qa_history_stub,
)
gen_fn = partial(
get_prompt_response,
client=client,
model=model,
prompt=prompt,
system_prompt=system_prompt,
max_tokens=longcepo_config.max_output_tokens,
temperature=longcepo_config.temperature_reduce,
)
reduce_result, upd_log = loop_until_match(gen_fn, answer_tags,)
cb_log.update(upd_log)
final_answer = reduce_result
for answer_tag in answer_tags:
if answer_tag in reduce_result:
final_answer = reduce_result.split(answer_tag)[-1].strip()
break
return final_answer, cb_log
def collapse_chunks(
client,
model: str,
context_chunks: List[str],
query: str,
system_prompt: str,
qa_history_stub: str,
tokenizer,
cb_log: CBLog,
longcepo_config: LongCepoConfig,
) -> Tuple[List[str], CBLog]:
"""
Collapses context chunk pairs in sliding window until the total token count fits within the context window.
Args:
client: LLM API client.
model (str): Base model name.
context_chunks (List[str]): Input context chunks.
query (str): User query.
system_prompt (str): System prompt string.
qa_history_stub (str): QA history prefix.
tokenizer: Tokenizer to compute token lengths.
cb_log (CBLog): Log object for tracking model calls.
longcepo_config (LongCepoConfig): Config with hyper-parameters and token limits.
Returns:
Tuple[List[str], CBLog]: Final context chunks and updated logs.
"""
num_tokens = get_prompt_length(format_chunk_list(context_chunks), tokenizer)
token_budget = (
longcepo_config.max_context_window
- get_prompt_length(longcepo_config.collapse_prompt, tokenizer)
- longcepo_config.max_output_tokens
)
logger.info(f"Pre-collapse length of chunks {num_tokens}, allowed {token_budget}")
def fetch_collapse_response(client, model, docs, query, system_prompt):
if len(docs) == 1:
return docs[0], CBLog()
return get_prompt_response(
client,
model,
longcepo_config.collapse_prompt.format(
question=query,
context="\n\n".join(docs),
qa_history_stub=qa_history_stub,
),
system_prompt,
max_tokens=longcepo_config.max_output_tokens,
temperature=longcepo_config.temperature_collapse,
)
merge_pair_idx = 0
collapse_step = 0
while num_tokens is not None and num_tokens > token_budget:
logger.info(f"Length at collapse stage {collapse_step}: {collapse_step}")
if len(context_chunks) == 1:
logger.info(f"Post-collapse length of chunks {num_tokens}")
return context_chunks, cb_log
# Merge chunk pair in a sliding window (merge_pair_idx:merge_pair_idx+2)
chunk_groups = (
[(context_chunks[i],) for i in range(merge_pair_idx)]
+ [(context_chunks[merge_pair_idx], context_chunks[merge_pair_idx + 1])]
+ [
(context_chunks[i],)
for i in range(merge_pair_idx + 2, len(context_chunks))
]
)
context_chunks, cb_log = concurrent_map(
fetch_collapse_response,
client,
model,
chunk_groups,
query,
system_prompt,
cb_log,
)
merge_pair_idx = (merge_pair_idx + 1) % max(len(context_chunks) - 1, 1)
num_tokens = get_prompt_length(format_chunk_list(context_chunks), tokenizer)
collapse_step += 1
logger.info(f"Post-collapse length of chunks {num_tokens}")
return context_chunks, cb_log
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